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Dig Deeper - Microbial Community Analysis

Project Background

Biodiversity loss, ecosystem degradation, and habitat destruction are increasingly linked to human-driven changes in land use, including urbanisation, agriculture, and the exploitation of natural resources (European Parliament, 2025; Jaureguiberry et al., 2022). In response, governments across Europe — including the EU — have introduced ambitious environmental strategies such as the EU Biodiversity Strategy for 2030 (European Parliament, 2025) and the 30x30 target (Markwick, 2023), which aims to protect 30% of land and sea by the year 2030.

Ecological restoration plays a vital role in addressing these challenges. Rather than simply returning ecosystems to a previous state, modern approaches focus on restoring ecological processes and enhancing ecosystem resilience (Hicks, 2023).

The RestREco Initiative

RestREco (Restoring Resilient Ecosystems) is a NERC-funded research project that adopts a resilience-based perspective on ecological restoration. The initiative brings together researchers from:

  • Cranfield University
  • University of Stirling
  • UK Centre for Ecology & Hydrology
  • The National Trust
  • Forest Research

Using a natural experiment design, RestREco studies a network of 133 ecological restoration sites across England and Scotland. The project aims to identify key drivers of ecosystem development, such as:

  • Time since restoration began
  • Initial ecological conditions
  • Proximity to existing woodland and grassland

The goal is to understand how these factors influence ecosystem complexity, function, and resilience to future pressures (RestREco, 2024).

The Dig Deeper Study

As part of the RestREco initiative, the Dig Deeper study focused on how the age of restoration, establishment type, and site management affect soil microbial communities, specifically bacteria and fungi.

To explore this, high-throughput sequencing was conducted on:

  • 16S rRNA gene (for bacterial communities)
  • ITS region (for fungal communities)

A total of 330 soil samples were collected for each marker.

For the bacteria (16S), sequencing produced an average of approximately 65,000 reads per sample, though this varied across the dataset:

  • A small number of samples had just over 30,000 reads
  • A few reached up to 85,000 reads

For the fungi (ITS), sequencing produced an average of approximately 65,000 reads per sample, though this varied across the dataset:

  • Two samples had just over 10,000 reads and a small number of samples had just over 30,000 reads
  • A few reached up to 90,000 reads

Despite this variation, sequencing depth was generally sufficient to support robust microbial analyses.

The analysis focused on three main aspects:

  • Alpha and beta diversity
  • Taxonomic composition
  • Functional diversity

These microbial assessments complement broader ecosystem-level measurements within the RestREco project, including vegetation, invertebrates, and ecosystem functions such as litter decomposition, pollination services, and soil thermodynamic efficiency.

Pipelines

The sequencing data were processed using the QIIME 2 bioinformatics platform — a widely used tool for microbiome analysis. Raw amplicon reads were denoised using the DADA2 plugin, enabling accurate identification of amplicon sequence variants (ASVs) with single-nucleotide resolution. This method improves upon traditional OTU clustering by enhancing precision.

After quality filtering and feature table construction, the pipeline proceeded to:

  • Generate taxonomic classifications
  • Perform alpha and beta diversity analyses
  • Create various interactive visualisations for exploring microbial community structure

Both 16S rRNA gene sequencing (for bacteria and archaea) and ITS sequencing (for fungi) were included, providing a broad overview of microbial diversity across samples.

16S Pipeline thumbnail

Figure 1. Pipeline (16S)

About the Data

About the Sites

The dataset encompasses 66 distinct sites, each contributing five soil samples. These sites span a wide age range — from 1 year to over 100 years — providing a valuable gradient for ecological comparisons.

Each site’s five samples:

  • Share the same establishment type
  • Are subject to the same management practices

In addition, soil pH and electrical conductivity (EC) were measured for every sample to help characterise environmental conditions.

There are:

  • 3 establishment types

  • 4 management types, which can be applied individually or in combination

    → Some sites follow a single management approach, while others incorporate two, three, or all four.

Mean Soil pH Across Sites

Soil pH is a critical environmental parameter that influences microbial community structure, nutrient availability, and overall ecosystem function.
This section summarises the average pH values for each sampled site, grouped by establishment type.

The bar chart below allows for easy comparison of mean pH across sites.
Each bar is coloured according to the establishment method (e.g., seed mix, natural regeneration, green hay), and by hovering over a bar, the user can view the precise pH value for each site.

Note: To improve clarity, site names have been removed from the x-axis, but full details are available via the interactive tooltip.

Figure 2. pH Mean For Each Site

Mean Electrical Conductivity

This section explores the variation in electrical conductivity (EC) across study sites.
Electrical conductivity is a measure of the soil’s ability to conduct electricity, often reflecting ion concentration and soil salinity, which can influence microbial activity and nutrient availability.

The plots below allow users to examine how EC differs depending on either the type of establishment or the age of the site.

Use the drop-down menu to switch between views. Hover over the bars for detailed site-specific values.

Figure 3. Mean EC per Site (by Establishment)

This plot shows the variation in electrical conductivity across sites, sorted by electrical conductivity.

Site Management Practices

This section illustrates the types of management practices applied at each site, including cutting, cattle grazing, sheep grazing, and ploughing.
Each coloured bar indicates the presence of one or more management strategies at a given site. Sites with multiple bars have undergone combinations of practices, highlighting the complexity and variation in land use across the study area.

Hover over each bar in the interactive plot to see the site name.

Figure 5. Management type for each site

Influence of Management Practices

This section examines how different management practices — such as cutting, grazing by cattle or sheep, and ploughing — influence two key soil properties: pH and electrical conductivity (EC).
These soil characteristics can affect microbial communities by altering nutrient availability, pH balance, and soil structure.

The visualisations below display the overall effect of each management type individually. However, it is important to note that potential interactions between management types (e.g., cutting combined with grazing) are not accounted for here.
Such interactions may play a significant role in shaping soil conditions but were beyond the scope of this visual summary.

Use the drop-down menu to explore how each management type affects pH or EC across all sampled sites.
Black dots represent the mean values for each management category.

Figure 6. pH Variation Depending on Management Type

QC and Pre-Processing

MutliQC on raw data

Bacteria (16S)

You can explore the full MultiQC report by clicking the image below:

MutlitQc thumbnail

Figure ??. MultiQC Plot (16S)

Fungi (ITS)

You can explore the full MultiQC report by clicking the image below:

MutlitQc thumbnail

Figure ??. MultiQC Plot (ITS)

QC after denoising

Bacteria (16S)

Here is a link to the statistics after denoising to view it on QIIME2 (16S) : Statitics after denoising (16S)

Figure ??. Statitics after denoising (16S)

Fungi (ITS)

Here is a link to the statistics after denoising to view it on QIIME2 (ITS) : Statitics after denoising (16S)

Figure ??. Statitics after denoising (ITS)

Alpha Diversity

Alpha diversity refers to the variety of organisms within a particular sample or environment. It reflects both richness—the number of distinct taxa—and evenness—how evenly individuals are distributed among those taxa. One of the most widely used measures for assessing alpha diversity is the Shannon index.

The Shannon index takes into account not only the number of species present, but also how evenly their abundances are distributed. A higher Shannon value generally indicates a more diverse and ecologically balanced community.

Another important metric is Faith’s Phylogenetic Diversity (Faith PD), which measures the total branch length of the phylogenetic tree that spans the species in a sample. Unlike the Shannon index, Faith PD incorporates evolutionary relationships, providing a phylogenetic perspective on diversity.

We also include Pielou’s Evenness index, which specifically quantifies how equally individual organisms are distributed across taxa. While Shannon integrates both richness and evenness, this metric isolates the evenness component, providing a complementary view of diversity patterns.

In the interactive plots below, we examine how the Shannon index, Faith PD and Evenness vary across different environmental and experimental conditions, separately for the 16S (bacteria and archaea) and ITS (fungi) datasets.

To allow interactive exploration of alpha diversity metrics across different environmental variables, we implemented a drop-down menu that dynamically displays the corresponding plots. Some variables, such as pH category, are only present in the ITS dataset, while others, like Year group, are specific to the 16S dataset. Internally, variables are mapped to their dataset-specific equivalents where needed (e.g. Age group in 16S becomes Age category in ITS). It is important to note, however, that these variables are not always directly comparable: for instance, Age group (16S) divides sites into multiple discrete intervals based on restoration age, while Age category (ITS) is a binary classification based on whether a site is above or below the median age. Despite these differences, the interface ensures that only available and relevant plots are shown for each selection.

For the ITS dataset, the alpha diversity was done per sample. In order to align it with the 16S analysis—where samples were already grouped by site—we aggregated the alpha diversity values by computing the mean per site. Categorical metadata was simplified using the most common (modal) value per site. This ensures consistency across datasets in the visual outputs. However, users interested in the original, unaggregated sample-level data can explore the full QIIME 2 results via the links provided under each section.

Shannon Index Boxplot

The boxplot below illustrate differences in Shannon diversity across groups. This metric reflects both species richness and how balanced the community is in terms of species abundance.

Bacteria (16S)

Kruskal-Wallis p-value: 0.000828

Here is a link to the full QIIME2 results (16S) : Shannon Index (16S)

Fungi (ITS)

Kruskal-Wallis p-value: 0.806

Here is a link to the full QIIME2 results (ITS) : Shannon Index (ITS)

Faith PD Boxplot

The following plots show Faith’s Phylogenetic Diversity, which integrates evolutionary relationships to capture how phylogenetically broad each microbial community is.

Kruskal-Wallis p-value: 0.0194

Here is a link to the full QIIME2 results (16S) : Faith PD (16S)

Evenness Boxplot

These boxplots display Pielou’s Evenness, highlighting how uniformly taxa are represented in each community. It allows us to isolate imbalance in dominance from richness effects.

Bacteria (16S)

Kruskal-Wallis p-value: 0.00576

Here is a link to the full QIIME2 results (16S) : Pielou Evenness (16S)

Fungi (ITS)

Kruskal-Wallis p-value: 0.519

Here is a link to the full QIIME2 results (ITS) : Pielou Evenness (ITS)

Spearman test (Bacteria - 16S)

Summary Table

Correlation Plots



Beta Diversity

To explore differences in microbial communities, we often rely on dimensionality reduction techniques such as Principal Coordinates Analysis (PCoA), visualised through Emperor plots. Two commonly used distance metrics in this context are Bray-Curtis and Jaccard.

While both metrics can reveal meaningful clustering and separation in microbial data, they capture complementary aspects of community structure.

Bacteria (16S)

Bray-Curtis Emperor Plot

The Bray-Curtis Emperor plot is a 3D visualisation of microbial community dissimilarities between samples, based on the Bray-Curtis distance. This distance metric quantifies how different two samples are in terms of species abundance, taking into account both presence/absence and relative abundances. It does not incorporate evolutionary relationships between features.

Using Principal Coordinates Analysis (PCoA), the high-dimensional Bray-Curtis distance matrix is projected into a lower-dimensional space—typically three axes—to capture the main patterns of variation across samples.

The Emperor plot is an interactive 3D tool developed for QIIME 2 that allows users to explore these PCoA results. Samples are represented as points in space, and their spatial proximity reflects ecological similarity:

  • Samples that are closer together have more similar microbial communities.
  • Samples that are further apart differ more strongly in community composition.

This type of plot is particularly useful for identifying clustering by experimental groups—such as treatment, site, or timepoint—and for detecting patterns or gradients in microbial diversity.

Figure 17. Bray-Curtis Emperor Plot

Here is a link to the Bray-Curtis Emperor Plot for more flexibility on QIIME2: Bray-Curtis Emperor Plot (16S)

Jaccard Emperor Plot

The Jaccard Emperor plot provides a 3D visualisation of microbial community dissimilarities based on the Jaccard distance. Unlike Bray-Curtis, the Jaccard metric considers only the presence or absence of features (e.g., microbial taxa) in each sample, ignoring their relative abundances.

This makes the Jaccard distance particularly suited for assessing community membership rather than abundance structure—focusing on which species are present, regardless of how abundant they are.

Using Principal Coordinates Analysis (PCoA), the high-dimensional Jaccard distance matrix is projected into a lower-dimensional space—usually three principal axes—to reveal major patterns in sample composition.

As with Bray-Curtis, the Emperor plot allows for interactive exploration of these ordinations:

  • Samples positioned closely together share more taxa in common (i.e., similar membership).
  • Samples far apart have fewer shared taxa, reflecting greater differences in species presence.

The Jaccard plot is useful when exploring factors that influence community membership, such as habitat type, land use, or environmental filtering—especially in studies where presence/absence patterns are more meaningful than relative abundances.

Figure 18. Jaccard Emperor Plot

Here is a link to the Jaccard Emperor Plot for more flexibility on QIIME2: Jaccard Emperor Plot (16S)

Fungi (ITS)

Bray-Curtis Emperor Plot

Figure 17. Bray-Curtis Emperor Plot

Here is a link to the Bray-Curtis Emperor Plot for more flexibility on QIIME2: Bray-Curtis Emperor Plot (ITS)

Jaccard Emperor Plot

Figure 18. Jaccard Emperor Plot

Here is a link to the Jaccard Emperor Plot for more flexibility on QIIME2: Jaccard Emperor Plot (ITS)

Taxonomy Composition

To explore the composition of soil microbial communities, we used Krona plots — interactive, circular charts that display taxonomic abundances in a hierarchical manner.

These plots allow users to intuitively navigate from broader taxonomic levels (such as Phylum) to more specific ones (like Genus), while simultaneously comparing relative abundances across taxa.

Bacteria (16S)

Krona Plots

In this study, Krona plots provide a powerful and user-friendly way to:

  • Visualise which microbial groups dominate each site
  • Explore the taxonomic diversity present in bacterial, archaeal, and fungal communities

You can click on the image below to access the Krona plots for each site.

Krona thumbnail

Figure 19. Krona Plot for Baltic_farm_1 (16S)

Fungi (ITS)

Krona Plots

You can click on the image below to access the Krona plots for each site.

Krona thumbnail

Figure 19. Krona Plot for Baltic_farm_1 (ITS)

Functional Diversity

In microbial ecology, a guild refers to a group of organisms that fulfil similar ecological roles, regardless of their taxonomic identity. Understanding functional guilds allows researchers to move beyond taxonomic profiles and assess the ecological roles that microbial communities may play in an environment.

Bacteria (16S)

Top Predicted Functional Pathways

The plot below displays the top 20 predicted KEGG pathways (or alternatively MetaCyc pathways) across all samples. These pathways reflect high-level metabolic functions such as amino acid metabolism, carbohydrate degradation, or environmental information processing.

Fungi (ITS)

To investigate the ecological roles of fungal communities, we used FUNGuild, a tool that assigns fungi to functional guilds based on curated databases and literature. These guilds represent ecological strategies such as:

  • Saprotrophs: decomposers of organic matter
  • Mycorrhizal fungi: symbionts associated with plant roots
  • Pathogens: organisms that cause disease in plants or animals
  • Ectomycorrhizal: symbionts that assist plant roots by enhancing nutrient and water uptake, especially nitrogen and phosphorus, while contributing to carbon cycling and soil nutrient mobilisation
  • Arbuscular Mycorrhizal: symbionts that contribute to nutrient uptake
  • Endophyte: microorganisms that promote the growth and development of the plants
  • Lichenized: fungi that form symbiotic associations with algae or cyanobacteria, creating lichens that can survive in harsh environments by combining structural support and photosynthetic ability
  • Parasites: fungi that live on or inside a host organism, extracting nutrients and often causing harm
  • Symbiotrophs: fungi that engage in mutually beneficial relationships with host organisms, typically exchanging nutrients for resources like carbon or protection

This functional classification provides valuable insights into what fungi are likely doing in the ecosystem, beyond simply who they are.

This section explores the functional roles of fungi within each site, based on guild-level annotations provided by FUNGuild. Fungal guilds reflect ecological functions such as saprotrophy, symbiosis (e.g., mycorrhizal fungi), or pathogenicity. This approach provides insight into how fungal communities may contribute to ecosystem processes, complementing traditional taxonomic analyses.

Top 20 Most Abundant Fungal Guilds

The plot below highlights the top 20 most abundant fungal guilds identified using FUNGuild. To avoid clutter, the guild names are hidden on the y-axis; however, users can hover over each bar to reveal the full name, enabling interactive and detailed exploration of fungal functional diversity.

Figure 20. Top 20 functional guilds

Fungal Guild Abundance Across Sites

The figure below shows the total abundance of fungal OTUs across sites, aggregated by functional guild. This provides an overview of how guild-level composition varies between locations, which may reflect differences in land use, soil conditions, or restoration histories.

Appendix